Motivated by the above analyses and recent computer vision models [1]}, especially Masked AutoEncoder (MAE) [2]}, we propose a masked autoencoding model for time series based on Transformer blocks (i.e., TSFormer).
TSFormer reconstructs the original signal based on the given partial observed signals.
We use an asymmetric design to largely reduce computation: the encoder operates on only partially visible signals, and the decoder uses a lightweight network on the full signals.
The model is shown in Figure REF (left), and we will introduce each component in detail next.
